IS = { zkontrolovano 24 Jan 2011 },
  UPDATE  = { 2011-01-14 },
   author =      {Knopp, Jan and Sivic, Josef and Pajdla, Tom{\'a}{\v s}},
   title =       {Avoding Confusing Features in Place Recognition},
   c_title =     {Odstran{\v e}n{\' i} matouc{\' i}ch p{\v r}{\' i}znaku
                  pro rozpozn{\' a}n{\' i} polohy pozorovatele},
   year =        {2010},
   pages =       {748-761},
   booktitle =   {Computer Vision - {ECCV 2010}, 11th European Conference on
                  Computer Vision, Proceedings, Part {I}},
   editor =      {Kostas Daniilidis and Petros Maragos and Nikos Paragios},
   publisher =   {Springer-Verlag},
   address =     {Berlin, Germany},
   isbn =        {978-3-642-15548-2},
   issn =        {0302-9743},
   series =      {LNCS},
   volume =      {6311},
   book_pages =  {811},
   month =       {September},
   day =         {5-11},
   venue =       {Hersonissos, Greece},
   organization ={Foundation for Research and Technology-Hellas (FORTH)},
   annote = {We seek to recognize the place depicted in a query image
   using a database of street side images annotated with geolocation
   information. This is a challenging task due to changes in scale,
   viewpoint and lighting between the query and the images in the
   database.  One of the key problems in place recognition is the
   presence of objects such as trees or road markings, which
   frequently occur in the database and hence cause significant
   confusion between different places. As the main contribution, we
   show how to avoid features leading to confusion of particular
   places by using geotags attached to database images as a form of
   supervision. We develop a method for automatic detection of
   image-specific and spatially-localized groups of confusing
   features, and demonstrate that suppressing them significantly
   improves place recognition performance while reducing the database
   size. We show the method combines well with the state of the art
   bag-of-features model including query expansion, and demonstrate
   place recognition that generalizes over wide range of viewpoints
   and lighting conditions.  Results are shown on a geotagged database
   of over 17K images of Paris downloaded from Google Street View.},
   c_annote =    {Pr{\' a}ce {\v r}e{\v s}{\' i} probl{\' e}m
   lokalizace kamery na z{\'a}klad{\v e} podobnosti
   obrazu. Hlavn{\'\i} p{\v r}{\' i}nos spo{\v c}{\' i}v{\' a} v
   odstran{\v e}n{\' i} matouc{\' i}ch {\v c}{\' a}st{\' i} p{\v r}{\'
   i}znakov{\' e}ho vektoru popisuj{\' i}c{\' i}ho pozorovan{\' e}
   m{\' i}sto. Metoda je demonstrov{\' a}na na datab{\' a}zi 17,000
   keywords =    {place recognition, feature selection, image search},
   prestige =    {important},
   authorship =  {34-33-33},
   project =     {FP7-SPACE-241523 PRoViScout only EU, SGS10/190/OHK3/2T/13},
   psurl =       {http://dx.doi.org/10.1007/978-3-642-15549-9_54},